I was helping translate some UI text for a website from English to German, my mother tongue. I found that usually the machine came up with better translations than me.
English and German are EU languages. Russian is not.
The EU maintains a large translation service to translate most EU official texts into all EU languages. So Google Translate is using that to train on. Google gets a free gift from a multinational bureaucracy and gets to look like a smart company in the process.
This is also why English-Mandarin is often poorly translated, in my opinion.
I am a professional translator, and I have been using LLMs to speed up and, yes, improve my translations for a year and a half.
When properly prompted, the LLMs produce reasonably accurate and natural translations, but sometimes there are mistakes (often the result of ambiguities in the source text) or the sentences don’t flow together as smoothly as I would like. So I check and polish the translations sentence by sentence. While I’m doing that, I sometimes encounter a word or phrase that just doesn’t sound right to me but that I can’t think how to fix. In those cases, I give the LLMs the original and draft translation and ask for ten variations of the problematic sentence. Most of the suggestions wouldn’t work well, but there are usually two or three that I like and that are better than what I could come up with on my own.
Lately I have also been using LLMs as editors: I feed one the entire source text and the draft translation, and I ask for suggestions for corrections and improvements to the translation. I adopt the suggestions I like, and then I run the revised translation through another LLM with the same prompt. After five or six iterations, I do a final read-through of the translation to make sure everything is okay.
My guess is that using LLMs like this cuts my total translation time by close to half while raising the quality of the finished product by some significant but difficult-to-quantify amount.
This process became feasible only after ChatGPT, Claude, and Gemini got longer context windows. Each new model release has performed better than the previous one, too. I’ve also tried open-weight models, but they were significantly worse for Japanese to English, the direction I translate.
Although I am not a software developer, I’ve been following the debates on HN about whether or not LLMs are useful as coding assistants with much interest. My guess is that the disagreements are due partly to the different work situations of the people on both sides of the issue. But I also wonder if some of those who reject AI assistance just haven’t been able to find a suitable interactive workflow for using it.
> While I’m doing that, I sometimes encounter a word or phrase that just doesn’t sound right to me but that I can’t think how to fix. In those cases, I give the LLMs the original and draft translation and ask for ten variations of the problematic sentence. Most of the suggestions wouldn’t work well, but there are usually two or three that I like and that are better than what I could come up with on my own.
Yes, coming up with variations that work better (and hit the right connotations) is what I used the machine for, too.
The EU maintains a large translation service to translate most EU official texts into all EU languages. So Google Translate is using that to train on. Google gets a free gift from a multinational bureaucracy and gets to look like a smart company in the process.
This is also why English-Mandarin is often poorly translated, in my opinion.